🚀 ComfyUI Auto-Installer — v5 (Python Rewrite)

Version 5 is a full rewrite from the ground up in Python, replacing all the PowerShell scripts from previous versions. It's cross-platform, faster, smarter, and now ships with a TUI manager, Docker images, and GPU-optimized inference out of the box.

If you are upgrading from the PowerShell version (v4.x), a one-command migration preserves all your models, outputs, and custom nodes: irm https://get.umeai.art/migrate.ps1 | iex

⚡ Quick Start (One-Liner)

• Windows (PowerShell): irm https://get.umeai.art/comfyui.ps1 | iex

• Linux / macOS: curl -fsSL https://get.umeai.art/comfyui.sh | sh

Only requires Git — everything else (Python, uv, dependencies) is handled automatically.

✨ What's New in v5

• Full Python rewrite — no more PowerShell dependency

• Cross-platform — Windows, Linux, macOS, and Docker

• TUI Manager — interactive terminal UI to launch, update, download models, and configure settings

• VRAM-aware model catalog — 7 model families with quantization recommendations based on your GPU

• GPU auto-detection — NVIDIA (CUDA 13.0/12.8), AMD (ROCm/DirectML), Apple Silicon (MPS)

• SageAttention 2 + 3 — pre-compiled wheels including RTX 50XX Blackwell support

• One-click update — update ComfyUI, all nodes, and dependencies with a single command

• Model security scanner — detects malicious pickle code in .ckpt/.pt files

• Junction architecture — models and outputs persist independently from ComfyUI updates

• Docker ready — 4 image variants including a cloud version with JupyterLab for RunPod

📋 Prerequisites

• Git

• GPU: NVIDIA (CUDA 12.x+), AMD (Radeon RX 6000+), or Apple Silicon (M1+)

• Internet connection

• Note: Python is automatically installed via uv if not present. No manual Python setup required.

🎨 Model Catalog (7 Families)

Interactive model downloader with VRAM-based recommendations (★ markers) and SHA-256 integrity checks. Each bundle offers multiple quantization variants (fp16, fp8, GGUF Q3→Q8). Downloads are accelerated via aria2c with HuggingFace + ModelScope fallback:

• FLUX (Image): Dev, Fill

• Z-IMAGE (Image): Turbo

• WAN 2.1 (Video): T2V, I2V 480p

• WAN 2.2 (Video): I2V, Fun Inpaint, Fun Camera

• HiDream (Image): Dev

• QWEN (Image Edit): Image Edit

• LTX-2 (Video + Audio): Dev

🧩 34 Custom Nodes Included

Additive manifest — never removes user-installed nodes.

• Core (always installed): ComfyUI-Manager

• UmeAiRT Tier: ComfyUI-UmeAiRT-Sync, ComfyUI-UmeAiRT-Toolkit, ComfyUI-Crystools, ComfyUI-nunchaku

• Full Tier (all of the above +): ComfyUI-Impact-Pack, ComfyUI-Impact-Subpack, ComfyUI-GGUF, ComfyUI-mxToolkit, ComfyUI-Custom-Scripts, ComfyUI-KJNodes, ComfyUI-WanVideoWrapper, ComfyUI-VideoHelperSuite, ComfyUI-Frame-Interpolation, rgthree-comfy, ComfyUI-Easy-Use, ComfyUI-HunyuanVideoMultiLora, ComfyUI-Florence2, ComfyUI-MultiGPU, ComfyUI-WanStartEndFramesNative, ComfyUI-Image-Saver, ComfyUI_UltimateSDUpscale, comfyui_controlnet_aux, x-flux-comfyui, ComfyUI-Detail-Daemon, wlsh_nodes, ComfyUI_essentials, ComfyUI-wanBlockswap, Derfuu_ComfyUI_ModdedNodes, ComfyUI_LayerStyle, ComfyUI-Upscaler-Tensorrt, comfyui-vrgamedevgirl, comfyui-int-and-float, was-node-suite-comfyui

⚙️ GPU Optimizations (Auto-Installed)

• PyTorch 2.10: CUDA 13.0/12.8, ROCm 7.1, DirectML, MPS

• xformers: Memory-efficient attention

• Triton: triton-windows / triton (Linux)

• SageAttention 2: Unified ABI3 wheels (Windows), per-arch SM80–SM100 (Linux)

• SageAttention 3: RTX 50XX Blackwell native (Windows + Linux)

• FlashAttention: Linux + NVIDIA only

• Nunchaku & InsightFace: Pre-compiled wheels

• Additional Python packages auto-installed: facexlib, onnxruntime-gpu, nvidia-ml-py, cupy-cuda13x, imageio-ffmpeg, hf_xet, cython, rotary_embedding_torch, blend_modes, segment_anything, gguf, and more.

🐳 Docker Support

Requires Docker and an NVIDIA GPU: docker run --gpus all -p 8188:8188 -v comfyui-data:/data registry.gitlab.com/umeairt-studio/comfyui-auto_installer-python:latest

• latest: ~4 GB — Ready to go with pre-installed PyTorch

• latest-cloud: ~4.5 GB — + JupyterLab for RunPod / cloud

• latest-lite: ~2 GB — Minimal (installs PyTorch on first run)

• latest-lite-cloud: ~2 GB — Lite + JupyterLab

🔒 Security

• No external script execution — all logic is internalized

• Secure subprocess calls — no shell=True

• HTTPS only — all URLs validated

• SHA-256 integrity checks on all model downloads

• Pickle model scanner — detects malicious code in .ckpt/.pt files

• Zip-slip prevention on archive extraction

• CI runs Bandit + pip-audit on every push

📂 Post-Installation

Three launcher scripts are generated:

• UmeAiRT-Start-ComfyUI: Launch (Performance mode + SageAttention)

• UmeAiRT-Start-ComfyUI_LowVRAM: Launch with --lowvram --fp8 for ≤8 GB VRAM

• UmeAiRT-Manager: TUI manager (update, download, reinstall, settings)

🔗 Links

• Source code: GitLab (https://gitlab.com/UmeAiRT-Studio/ComfyUI-Auto_installer-Python)

• Mirror: Codeberg (https://codeberg.org/UmeAiRT)

• Ecosystem: UmeAiRT Studio (https://umeai.art)